RAG-Driven Generative AI provides a roadmap for building effective LLM, computer vision, and generative AI systems that balance performance and costs. This book offers a detailed exploration of RAG and how to design, manage, and control multimodal AI pipelines. By connecting outputs to traceable source documents, RAG improves output accuracy and contextual relevance, offering a dynamic approach to managing large volumes of information. This AI book also shows you how to build a RAG framework, providing practical knowledge on vector stores, chunking, indexing, and ranking. You'll discover techniques to optimize your project's performance and better understand your data, including using adaptive RAG and human feedback to refine retrieval accuracy, balancing RAG with fine-tuning, implementing dynamic RAG to enhance real-time decision-making, and visualizing complex data with knowledge graphs. You'll be exposed to a hands-on blend of frameworks like LlamaIndex and Deep Lake, vector databases such as Pinecone and Chroma, and models from Hugging Face and OpenAI. By the end of this book, you will have acquired the skills to implement intelligent solutions, keeping you competitive in fields ranging from production to customer service across any project.

• Scale RAG pipelines to handle large datasets efficiently
• Employ techniques that minimize hallucinations and ensure accurate responses
• Implement indexing techniques to improve AI accuracy with traceable and transparent outputs
• Customize and scale RAG-driven generative AI systems across domains
• Find out how to use Deep Lake and Pinecone for efficient and fast data retrieval
• Control and build robust generative AI systems grounded in real-world data
• Combine text and image data for richer, more informative AI responses

This repo is continually updated and upgraded.
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